Use of Two Pain Assessment Tools to Improve Pain Recognition in Post Stroke Patients: A Quality Improvement Project
Bibliographic record
Abstract
Abstract Background Post stroke patients can suffer from a variety of complex pain syndromes. These are often associated with decline in function, cognitive impairment, impaired quality of life and mood disturbances. Pain identification can be challenging in this population, due to stroke deficits that impair communication, and associated medical conditions that can affect the patient’s ability to self-report pain. This Quality Improvement (QI) project aims at implementing two pain assessment tools to support clinicians assess pain in a structured manner, and thereby improve pain management. Methods An initial audit was performed to identify whether post stroke pain (PSP) was being recorded in a reliable manner in St Mary's hospital. A pain assessment proforma was then designed, incorporated in the admission proforma and rolled-out for use. This included two pain assessment tools: the Numerical Pain Scale for patients who could self-report pain, and the Abbey Pain Scale for patients with communication difficulties. A second audit was performed to analyse the compliance to documentation and a qualitative evaluation of its use was also conducted in semi-structured staff interviews. Results The first audit showed pain being under reported with only 25% of patients having formal pain assessment documented on admission. The second audit showed improvement in pain assessment with 72.7% pain documentation. Qualitative evaluation revealed staff expressing challenges with pain identification in post stroke patients and viewed the proforma as a useful prompt. Limitations to the use of observational tools included time constraints, lack of familiarity with tools and potential inaccuracy of scores. Conclusion Identification of PSP is key to optimising treatment, and hence improving function and quality of life of stroke patients. This QI project shows that the use of pain assessment tools, in combination with detailed clinical assessment can help pain assessment. Clinician awareness and continuous training are required to support this challenging task.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".